978 resultados para Immersive learning
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Audit report on the Muscatine Agricultural Learning Center for the year ended December 31, 2013
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Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.
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The performance of magnetic nanoparticles is intimately entwined with their structure, mean size and magnetic anisotropy. Besides, ensembles offer a unique way of engineering the magnetic response by modifying the strength of the dipolar interactions between particles. Here we report on an experimental and theoretical analysis of magnetic hyperthermia, a rapidly developing technique in medical research and oncology. Experimentally, we demonstrate that single-domain cubic iron oxide particles resembling bacterial magnetosomes have superior magnetic heating efficiency compared to spherical particles of similar sizes. Monte Carlo simulations at the atomic level corroborate the larger anisotropy of the cubic particles in comparison with the spherical ones, thus evidencing the beneficial role of surface anisotropy in the improved heating power. Moreover we establish a quantitative link between the particle assembling, the interactions and the heating properties. This knowledge opens new perspectives for improved hyperthermia, an alternative to conventional cancer therapies.
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The aim of this questionnaire is to assess your experience of the DEL learning process as developed by your university. The questionnaire is anonymous and it should not take more than fifteen minutes to complete.
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The Institute has professionals with extensive experience in training, specifically with experience in the field of police and emergencies training. Moreover, it also has very talented people. But above all, our institution has public professionals with a desire to serve, who love security and emergency responders and want to provide them with the best knowledge to make them every day better professionals. In the quest for continuous training improvement, its during 2009 when e-learning begins to have a presence at the Institute. Virtual training methodology becomes a facilitator for the training of various professionals, avoiding geographical displacement and easing the class schedule.
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Individual learning (e.g., trial-and-error) and social learning (e.g., imitation) are alternative ways of acquiring and expressing the appropriate phenotype in an environment. The optimal choice between using individual learning and/or social learning may be dictated by the life-stage or age of an organism. Of special interest is a learning schedule in which social learning precedes individual learning, because such a schedule is apparently a necessary condition for cumulative culture. Assuming two obligatory learning stages per discrete generation, we obtain the evolutionarily stable learning schedules for the three situations where the environment is constant, fluctuates between generations, or fluctuates within generations. During each learning stage, we assume that an organism may target the optimal phenotype in the current environment by individual learning, and/or the mature phenotype of the previous generation by oblique social learning. In the absence of exogenous costs to learning, the evolutionarily stable learning schedules are predicted to be either pure social learning followed by pure individual learning ("bang-bang" control) or pure individual learning at both stages ("flat" control). Moreover, we find for each situation that the evolutionarily stable learning schedule is also the one that optimizes the learned phenotype at equilibrium.
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This article offers a review of the literature on interprofessional education (EIP), a form of education which brings together members of two or more professions in a joint training. In this course, participants gain knowledge through other professionals and about them. The goal of EIP is to improve collaboration between health professionals and the quality of patient care. The EIP is booming worldwide and seems for from a mere fad. This expansion can be explained by several factors: the increasing importance attributed to the quality of care and patient safety, care changes (aging population and increasing chronic diseases) and the shortage of health professionals. The expectations of the EIP are large, while the evidence supporting its effectiveness is being built.
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The purpose of this study is to answer the principal question, Do Real Books help the learning of English in P4?, and create a useful document to English teachers in Infant Education that are interested in working through stories. Previous to the development of the work, it has been necessary a research work and a theoretical documentation, performed using a literature review. This process has served to establish the basis of the study and to develop the subsequent didactic intervention. This second part of the study, the didactic intervention, contains all the information about how it was planned, carried to term and evaluated. There are three different ‘real books’ used as a reference in which the reader can see how to choose the correct story, how to adapt the text to the children, the daily planning, and how to evaluate the activities.
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Video games in a learning environ-ment and their application to the educational world has become a common study between researchers because the use of videogames in the classroom is coherent with a compe-tence-based education theory. A social science approach to video-games allows an exhaustive under-standing of the video games and of the game experience in the classroom. When a video game gives to the user a high level of immersion, all his interest and energy is focused in the game, this fact together with the motivation in the video game is the basis of interest for educators and researchers in the potentiality of the video games as learning environ-ments. In this presentation, we will show and analyze the importance of the video games in an educational context, using a case study: the video game "Monturiol el joc". In the paper, we describe the video game structure, the instructional function, the practice application and the future work. Also, we discuss the impor-tance of the video games as a learning immersive environment, and we conclude our report with a brief analysis of the importance of the study of video games.
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The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.
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El objetivo de este artículo es presentar el proyecto EcoSPORTech, cuya finalidad es la creación de una empresa social con jóvenes para la realización de actividades deportivas/ocio en el medio natural, integrando las nuevas tecnologías. Este proyecto supone una colaboración interdisciplinaria dentro de la Universidad de Vic, entre las facultades de Empresa y Comunicación (FEC), la de Ciencias de la Salud y el Bienestar (FCSB) y la de Educación (FE) e integra un equipo de profesionales procedentes de los ámbitos de la empresa, el marketing, el periodismo, el deporte y la terapia ocupacional. Estos profesores formarán al grupo de jóvenes con los que se creará la empresa y dirigirán la misma. Esta empresa (cooperativa) se integra en el vivero de empresas sociales que se está creando en la Universidad de Vic.
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The Learning Affect Monitor (LAM) is a new computer-based assessment system integrating basic dimensional evaluation and discrete description of affective states in daily life, based on an autonomous adapting system. Subjects evaluate their affective states according to a tridimensional space (valence and activation circumplex as well as global intensity) and then qualify it using up to 30 adjective descriptors chosen from a list. The system gradually adapts to the user, enabling the affect descriptors it presents to be increasingly relevant. An initial study with 51 subjects, using a 1 week time-sampling with 8 to 10 randomized signals per day, produced n = 2,813 records with good reliability measures (e.g., response rate of 88.8%, mean split-half reliability of .86), user acceptance, and usability. Multilevel analyses show circadian and hebdomadal patterns, and significant individual and situational variance components of the basic dimension evaluations. Validity analyses indicate sound assignment of qualitative affect descriptors in the bidimensional semantic space according to the circumplex model of basic affect dimensions. The LAM assessment module can be implemented on different platforms (palm, desk, mobile phone) and provides very rapid and meaningful data collection, preserving complex and interindividually comparable information in the domain of emotion and well-being.
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Both, Bayesian networks and probabilistic evaluation are gaining more and more widespread use within many professional branches, including forensic science. Notwithstanding, they constitute subtle topics with definitional details that require careful study. While many sophisticated developments of probabilistic approaches to evaluation of forensic findings may readily be found in published literature, there remains a gap with respect to writings that focus on foundational aspects and on how these may be acquired by interested scientists new to these topics. This paper takes this as a starting point to report on the learning about Bayesian networks for likelihood ratio based, probabilistic inference procedures in a class of master students in forensic science. The presentation uses an example that relies on a casework scenario drawn from published literature, involving a questioned signature. A complicating aspect of that case study - proposed to students in a teaching scenario - is due to the need of considering multiple competing propositions, which is an outset that may not readily be approached within a likelihood ratio based framework without drawing attention to some additional technical details. Using generic Bayesian networks fragments from existing literature on the topic, course participants were able to track the probabilistic underpinnings of the proposed scenario correctly both in terms of likelihood ratios and of posterior probabilities. In addition, further study of the example by students allowed them to derive an alternative Bayesian network structure with a computational output that is equivalent to existing probabilistic solutions. This practical experience underlines the potential of Bayesian networks to support and clarify foundational principles of probabilistic procedures for forensic evaluation.